‘Vincent’ AI completes sketches, inspired by history of art

Image credit: Cambridge Consultants

By Hilary Lamb

Published Thursday, September 21, 2017

Named after the famous Dutch artist, ‘Vincent’ uses machine learning methods to enhance a human sketch to create a piece of digital art. It was trained with thousands of pieces: the ‘digested sum’ of post-Renaissance art.

Vincent was created by data scientists and engineers at design and development company Cambridge Consultants’ “Digital Greenhouse” facility.

Machine learning methods – which train a computer to recognise patterns based on thousands of images, videos, audio clips, movements or other forms of data – have been used in the arts to produce music inspired by particular composers and even write film scripts.

Typical machine learning approaches to art use mathematics to entirely generate “approximations of art”. However, this is the first instance of machine learning software “interpreting” a human sketch and using this as guidance to create a final piece of art.

Vincent is based on multiple Generative Adversarial Networks: a recent class of algorithms used in unsupervised machine learning, often exploited to generate photorealistic images. It was trained on thousands of pieces of art from the Renaissance (including masterpieces of Van Gogh, Cézanne and Picasso), and through the process, came to recognise patterns in texture, colour and contrast.

Following the training, Vincent was able to interpret edges in paintings, and use these edges to produce completed pictures from a simple line drawing composed on a tablet.

“What we’ve built would have been unthinkable to the original deep learning pioneers,” said Monty Barlow, machine learning director at Cambridge Consultants.

“By successfully combining different machine learning approaches, such as adversarial training, perceptual loss, and end-to-end training of stacked networks, we’ve created something hugely interactive, taking the germ of a sketches idea and allowing the history of human art to run with it.”

According to researchers, software similar to Vincent could have applications in varied sectors, such as digital security or autonomous vehicles, where technology could generate training scenarios and simulations based on simple human input. This would allow for the quick generation of almost limitless variety in scenarios.

“We’re exploring completely uncharted territory – much of what makes Vincent tick was not known to the machine learning community just a year ago,” said Barlow.

“We’re excited to be at the leading edge of an emerging, transformative industry and to be making the leap from the art of the possible to delivering practical machine learning solutions for our clients.”